Resum:

In this research, an overnight parcel logistics company's (hereby referred to as OPLC) capillary distribution network will be modelled by stochastic techniques. Specifically, the capillary distribution networks this parcel logistics company has in Sant Cugat del Vallés . A capillary distribution network is com posed by the routes and the vehicles in charge of the delivery and collection of parcels to the final client or point of sale. It is also known as last mile distribution.
The OPLC needs to design a capillary distribution network that is capable of collecting all the merchandise that their clients wish to deliver, and, at the same time, have the capacity to distribute all shipments to their destination. To reach this goal, the designers have to decide the type of vehicles (van, lorry or trailer) and the quantity of each type of vehicle the capillary distribution network needs to cover the area around each hub efficiently. This number of vehicles has to satisfy the delivery/collection quality requirements with the minim um cost to guarantee the maxim um profit for the OPLC.
The aim of this research is to explore and analyses how to design the capillary distribution network, and to demonstrate that regression analysis and other stochastic techniques are va lid techniques to help an OPLC in the decision making related to the design of capillary distribution network. Most models related to logistics companies have been based on deterministic techniques so far. In this research regression analysis and the adjustment of the variables by probability distribution functions are used instead the deterministic techniques in the design of the capillary distribution network.
This design will be based on assigning a distribution area to each vehicle, as opposed to a determined route of collections and deliveries for each vehicle, as has been done until now in the vehicle routing problem. The aim of assigning each vehicle to specific distribution areas is to guarantee a minimum income for the driver in charge of this distribution area, thus to ensure the continuity of the dri'.13r in the company. This continuity maximizes driver familiarity within their distribution area. With increased familiarity, driver perform anee improves due to ease in finding addresses and locations as well as efficiency in organizing daily routes. Their capacity to make deliveries and collections increases, and therefore, so does their productivity.
The distribution network will be divided into postcode areas and the income generated in each one will be estimated. According to that income, the number of drivers assigned to each post code will be determined.
The way the regression analysis are undertaken is by maxim um likelihood. Maxim um likelihood technique allows com paring different regression models, each one with its own characteristics. Moreover, maximum likelihood technique allows fulfilling the parsimony principle, and determining which exogenous variables affect in a significative way the endogenous variable that is wanted to be modelled, by likelihood ratio test. In this research, three different regress ion models are undertaken for each postcode area: ordinary least squares, generalized linear models and errors distributed with no normal probability distribution functions. Ali of them are compared by their maxim um likelihood values ,and the one with best results in each area is used to estimate driver's income in the corresponding distribution area. After the analysis of the results, it has been verified satisfactorily that a stochastic modeling by regression techniques is valid to estimate the cost of the different post code analyzed, and gives the possibility of estimate the income of the driver of each one.
Among the different regress ion techniques used in this research, the models run under the assumption that the errors are distributed with no normal probability distribution functions have given the best results